Triggering storage of electrocardiographs for detected premature ventricular contractions (pvcs)

ABSTRACT

Techniques for triggering the storage or transmission of cardiac electrogram (EGM) signals associated with a premature ventricular contractions (PVC) include sensing a cardiac EGM signal of a patient via a plurality of electrodes, detecting a premature ventricular contraction (PVC) within the cardiac EGM signal, determining whether PVC storage criteria is met, in response to a determination that the PVC storage criteria is met, storing a portion of the cardiac EGM signal associated with the PVC, and in response to a determination that the PVC storage criteria is not met, eschewing storing the portion of the cardiac EGM signal associated with the PVC.

This application claims the benefit of U.S. Provisional PatentApplication No. 62/927,932, filed on Oct. 30, 2019, the entire contentof which is incorporated herein by reference.

FIELD

The disclosure relates generally to medical device systems and, moreparticularly, medical device systems configured to detect prematureventricular contractions (PVCs).

BACKGROUND

Medical devices may be used to monitor physiological signals of apatient. For example, some medical devices are configured to sensecardiac electrogram (EGM) signals indicative of the electrical activityof the heart via electrodes. Some medical devices may be configured todeliver a therapy in conjunction with or separate from the monitoring ofphysiological signals.

PVCs are premature heartbeats. PVCs are premature because they occurbefore the regular heartbeat. During a PVC event, the ventricleselectrically discharge and contract prematurely before the normalelectrical discharge arrives from the sinoatrial node. PVCs may occur inhealthy individuals. PVCs may be caused by caffeine, smoking, alcoholconsumption, stress, exhaustion, pharmacological toxicity, electrolyteimbalance, lack of oxygen, and heart attack as examples. Common symptomsassociated with PVCs include palpitations, dizziness, fatigue, dyspnea,chest pain, and lightheadedness. PVCs are normally considered benign,but may potentially cause cardiomyopathy, ventricular arrythmias, andheart failure.

Management strategies for PVC induced cardiomyopathy include medicaltherapy and catheter ablation, with an increasing role for catheterablation in view of the potential for permanent suppression of PVCs.Ablation to suppress PVCs may lead to improvement of left ventricularsystolic dysfunction (LVSD) and normalization of left ventricularejection fraction (LVEF). PVC burden, i.e., a quantification of theamount of PVCs over a period of time, can be an independent predictor ofPVC induced cardiomyopathy. Presently, 24-hour Holter monitoring is themost commonly used method to determine PVC burden.

SUMMARY

In general, this disclosure is directed to techniques for detecting PVCsusing a medical device. More particularly, the disclosure is directed totechniques for triggering the storage or transmission of cardiac EGMsignals associated with a PVC in response to one or more PVC storagecriteria being met. For example, processing circuitry of an implantablemedical device (IMD) or another device may identify a PVC in a cardiacEGM signal, classify the PVC, and store or transmit the PVC signal to aserver or remote computing device when a PVC burden is above a PVCburden threshold or when a new PVC classification is detected. In thisway, the processing circuitry may only store or transmit the cardiac EGMsignals necessary to aid a physician while conserving storage space andbattery life of the device by not storing or transmitting every singledetected PVC. Moreover, storing or transmitting PVCs in response to thePVC burden for a given patient exceeding the PVC burden threshold mayhelp determine that the patient is experiencing one or more patientconditions such as such as risk of sudden cardiac death, arrhythmias, orcardiomyopathy.

EGM signals, in some cases, may indicate one or more events of a heartcycle such as ventricular depolarizations and/or repolarizations, atrialdepolarizations and/or repolarizations, or any combination thereof. SuchEGMs may be referred to as cardiac EGMs or cardiac EGM signals. PVCs canbe detected in cardiac EGM signals. While PVCs are common and usuallyharmless, they can be dangerous for persons with existing heartproblems. Therefore, it may be helpful for physicians to detect andidentify patterns of PVCs to better treat their patients, particularlypatients with existing heart problems. For example, physicians may wantto view and analyze EGM data about the morphologies of the PVCs detectedin a particular patient. This data can include exemplary PVCmorphologies, classes of PVC morphologies, or morphologies of PVCsoccurring within a period of time (e.g., a day, a week, a month) or inparticular intervals of time (e.g., hourly, daily, weekly). By providingexemplary morphologies or classes of PVC morphologies detected in apatient to a physician, a system in accordance with this disclosure mayassist a physician to localize the origin of the PVCs and/or determinewhether there are multiple triggers causing the PVCs. This mayfacilitate more accurate determinations of cardiac wellness and risk ofsudden cardiac death, and may lead to clinical interventions to suppressPVCs such as medications and PVC ablations of targeted areas of theheart.

In one example, a medical system comprises a plurality of electrodesconfigured to sense a cardiac electrogram (EGM) signal of a patient; andprocessing circuitry configured to detect a premature ventricularcontraction (PVC) within the cardiac EGM signal; determine whether PVCstorage criteria is met; in response to a determination that the PVCstorage criteria is met, store a portion of the cardiac EGM signalassociated with the PVC; and in response to a determination that the PVCstorage criteria is not met, eschew storing the portion of the cardiacEGM signal associated with the PVC.

In another example, a method comprises sensing a cardiac electrogram(EGM) signal of a patient via a plurality of electrodes; detecting apremature ventricular contraction (PVC) within the cardiac EGM signal;determining whether PVC storage criteria is met; in response to adetermination that the PVC storage criteria is met, storing a portion ofthe cardiac EGM signal associated with the PVC; and in response to adetermination that the PVC storage criteria is not met, eschewingstoring the portion of the cardiac EGM signal associated with the PVC.

In another example, a non-transitory computer-readable medium comprisinginstructions for causing one or more processors to sense a cardiacelectrogram (EGM) signal of a patient; detect a premature ventricularcontraction (PVC) within the cardiac EGM signal; determine whether PVCstorage criteria is met; in response to a determination that the PVCstorage criteria is met, store a portion of the cardiac EGM signalassociated with the PVC; and in response to a determination that the PVCstorage criteria is not met, eschew storing the portion of the cardiacEGM signal associated with the PVC.

The summary is intended to provide an overview of the subject matterdescribed in this disclosure. It is not intended to provide an exclusiveor exhaustive explanation of the systems, device, and methods describedin detail within the accompanying drawings and description below.Further details of one or more examples of this disclosure are set forthin the accompanying drawings and in the description below. Otherfeatures, objects, and advantages will be apparent from the descriptionand drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the environment of an example medical system inconjunction with a patient.

FIG. 2 is a functional block diagram illustrating an exampleconfiguration of the implantable medical device (IMD) of the medicalsystem of FIG. 1.

FIG. 3 is a conceptual side-view diagram illustrating an exampleconfiguration of the IMD of FIGS. 1 and 2.

FIG. 4 is a functional block diagram illustrating an exampleconfiguration of the external device of FIG. 1.

FIG. 5 is a block diagram illustrating an example system that includesan access point, a network, external computing devices, such as aserver, and one or more other computing devices, which may be coupled tothe IMD and external device of FIGS. 1-4.

FIG. 6A is a graph illustrating a cardiac EGM signal and an exampletechnique for detecting and classifying PVCs based on cardiac EGMsignal.

FIG. 6B illustrates exemplary classified PVC cardiac EGM signals.

FIG. 7 is a flow diagram illustrating an example operation fortriggering the storage of a cardiac EGM signal associated with a PVC.

FIG. 8 is a flow diagram illustrating an example operation fortriggering the storage of a portion of a cardiac EGM signal associatedwith a PVC.

FIG. 9 is a flow diagram illustrating an example operation forclassifying PVCs.

FIG. 10 is a graph illustrating example PVC information that may bepresented to a user.

Like reference characters denote like elements throughout thedescription and figures.

DETAILED DESCRIPTION

A variety of types of medical devices sense cardiac EGMs. Some medicaldevices that sense cardiac EGMs are non-invasive, e.g., using aplurality of electrodes placed in contact with external portions of thepatient, such as at various locations on the skin of the patient. Theelectrodes used to monitor the cardiac EGM in these non-invasiveprocesses may be attached to the patient using an adhesive, strap, belt,or vest, as examples, and electrically coupled to a monitoring device,such as an electrocardiograph, Holter monitor, or other electronicdevice. The electrodes are configured to sense electrical signalsassociated with the electrical activity of the heart or other cardiactissue of the patient, and to provide these sensed electrical signals tothe electronic device for further processing and/or display of theelectrical signals. The non-invasive devices and methods may be utilizedon a temporary basis, for example to monitor a patient during a clinicalvisit, such as during a doctor's appointment, or for example for apredetermined period of time, for example for one day (twenty-fourhours), or for a period of several days.

External devices that may be used to non-invasively sense and monitorcardiac EGMs include wearable devices with electrodes configured tocontact the skin of the patient, such as patches, watches, or necklaces.One example of a wearable physiological monitor configured to sense acardiac EGM is the SEEQ™ Mobile Cardiac Telemetry System, available fromMedtronic plc, of Dublin, Ireland. Such external devices may facilitaterelatively longer-term monitoring of patients during normal dailyactivities, and may periodically transmit collected data to a networkservice, such as the Medtronic Carelink™ Network.

Some implantable medical devices (IMDs) also sense and monitor cardiacEGMs. The electrodes used by IMDs to sense cardiac EGMs are typicallyintegrated with a housing of the IMD and/or coupled to the IMD via oneor more elongated leads. Example IMDs that monitor cardiac EGMs includepacemakers and implantable cardioverter-defibrillators, which may becoupled to intravascular or extravascular leads, as well as pacemakerswith housings configured for implantation within the heart, which may beleadless. An example of pacemaker configured for intracardiacimplantation is the Micra™ Transcatheter Pacing System, available fromMedtronic plc. Some IMDs that do not provide therapy, e.g., implantablepatient monitors, sense cardiac EGMs. One example of such an IMD is theReveal LINQ™ Insertable Cardiac Monitor, available from Medtronic plc,which may be inserted subcutaneously. Such IMDs may facilitaterelatively longer-term monitoring of patients during normal dailyactivities, and may periodically transmit collected data to a networkservice, such as the Medtronic Carelink™ Network.

Any medical device configured to sense a cardiac EGM via implanted orexternal electrodes, including the examples identified herein, mayimplement the techniques of this disclosure for detecting a PVC in acardiac EGM, classifying the PVC, and storing a portion of the cardiacEGM signal associated with PVC when one or more PVC storage criteria ismet. The techniques of this disclosure for triggering storage of cardiacEGM signals associated with a PVC may facilitate determinations ofcardiac wellness and risk of sudden cardiac death, and may lead toclinical interventions to suppress PVCs such as medications and PVCablations.

FIG. 1 illustrates the environment of an example medical system 2 inconjunction with a patient 4, in accordance with one or more techniquesof this disclosure. The example techniques may be used with an IMD 10,which may be in wireless communication with at least one of externaldevice 12 and other devices not pictured in FIG. 1. In some examples,IMD 10 is implanted outside of a thoracic cavity of patient 4 (e.g.,subcutaneously in the pectoral location illustrated in FIG. 1). IMD 10may be positioned near the sternum near or just below the level of theheart of patient 4, e.g., at least partially within the cardiacsilhouette. IMD 10 includes a plurality of electrodes (not shown in FIG.1), and is configured to sense a cardiac EGM via the plurality ofelectrodes. IMD 10 may transmit data to external device 12 (or any otherdevice). The transmitted data may include values of physiologicalparameters measured by IMD 10, indications of episodes of arrhythmia orother maladies detected by IMD 10, and physiological signals recorded byIMD 10. For example, external device 12 may receive information relatedto detection of PVCs by IMD 10, such as at least a portion of a cardiacEGM signal, morphological information about PVCs or a count or otherquantification of PVCs, e.g., over a time period. In some examples, IMD10 may transmit cardiac EGM segments due to IMD 10 determining that anepisode of arrhythmia or another malady occurred during the segment, orin response to a request to record the segment from patient 4 or anotheruser. In some examples, IMD 10 takes the form of the LINQ™ ICM, oranother ICM similar to, e.g., a version or modification of, the LINQ™ICM.

External device 12 may be a computing device with a display viewable bythe user and an interface for providing input to external device 12(i.e., a user input mechanism). In some examples, external device 12 maybe a notebook computer, tablet computer, workstation, one or moreservers, cloud, data center, cellular phone, personal digital assistant,or another computing device that may run an application that enables thecomputing device to interact with IMD 10. External device 12 isconfigured to communicate with IMD 10 and, optionally, another computingdevice (not illustrated in FIG. 1), via wireless communication. Externaldevice 12, for example, may communicate via near-field communicationtechnologies (e.g., inductive coupling, NFC or other communicationtechnologies operable at ranges less than 10-20 cm) and far-fieldcommunication technologies (e.g., RF telemetry according to the 802.11or Bluetooth® specification sets, or other communication technologiesoperable at ranges greater than near-field communication technologies).

External device 12 may be used to configure operational parameters forIMD 10. External device 12 may be used to retrieve data from IMD 10. Theretrieved data may include values of physiological parameters measuredby IMD 10, indications of episodes of arrhythmia or other maladiesdetected by IMD 10, and physiological signals recorded by IMD 10. Forexample, external device 12 may retrieve information related todetection of PVCs by IMD 10, such as at least a portion of a cardiac EGMsignal, morphological information about PVCs or a count or otherquantification of PVCs, e.g., over a time period since the lastretrieval of information by external device. External device 12 may alsoretrieve cardiac EGM segments recorded by IMD 10, e.g., due to IMD 10determining that an episode of arrhythmia or another malady occurredduring the segment, or in response to a request to record the segmentfrom patient 4 or another user. As discussed in greater detail belowwith respect to FIG. 5, one or more remote computing devices mayinteract with IMD 10 in a manner similar to external device 12, e.g., toprogram IMD 10 and/or exchange data with IMD 10, via a network.

Processing circuitry of medical system 2, e.g., of IMD 10, externaldevice 12, and/or of one or more other computing devices, may beconfigured to perform the example techniques of this disclosure fordetecting a PVC, classifying the PVC, and, in response to one or morePVC storage criteria being met, triggering the storage or transmissionof cardiac EGM information associated with the PVC. In some examples,the processing circuitry of medical system 2 analyzes a cardiac EGMsignals sensed by IMD 10 to determine whether a PVC has occurred. ThePVC storage criteria may include a PVC burden being above a PVC burdenthreshold or when a new PVC classification is detected, as described ingreater detail below. Although described in the context of examples inwhich IMD 10 that senses the cardiac EGM comprises an insertable cardiacmonitor, example systems including one or more implantable or externaldevices of any type configured to sense a cardiac EGM may be configuredto implement the techniques of this disclosure.

FIG. 2 is a functional block diagram illustrating an exampleconfiguration of IMD 10 of FIG. 1 in accordance with one or moretechniques described herein. In the illustrated example, IMD 10 includeselectrodes 16A and 16B (collectively “electrodes 16”), antenna 26,processing circuitry 50, sensing circuitry 52, communication circuitry54, storage device 56, switching circuitry 58, and sensors 62. Althoughthe illustrated example includes two electrodes 16, IMDs including orcoupled to more than two electrodes 16 may implement the techniques ofthis disclosure in some examples.

Processing circuitry 50 may include fixed function circuitry and/orprogrammable processing circuitry. Processing circuitry 50 may includeany one or more of a microprocessor, a controller, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or equivalent discrete or analoglogic circuitry. In some examples, processing circuitry 50 may includemultiple components, such as any combination of one or moremicroprocessors, one or more controllers, one or more DSPs, one or moreASICs, or one or more FPGAs, as well as other discrete or integratedlogic circuitry. The functions attributed to processing circuitry 50herein may be embodied as software, firmware, hardware or anycombination thereof.

Sensing circuitry 52 may be selectively coupled to electrodes 16 viaswitching circuitry 58, e.g., to select the electrodes 16 and polarity,referred to as the sensing vector, used to sense a cardiac EGM, ascontrolled by processing circuitry 50. Sensing circuitry 52 may sensesignals from electrodes 16, e.g., to produce a cardiac EGM, in order tofacilitate monitoring the electrical activity of the heart. Sensingcircuitry 52 also may monitor signals from sensors 62, which may includeone or more accelerometers, pressure sensors, and/or optical sensors, asexamples. In some examples, sensing circuitry 52 may include one or morefilters and amplifiers for filtering and amplifying signals receivedfrom electrodes 16 and/or sensors 62.

Sensing circuitry 52 and/or processing circuitry 50 may be configured todetect cardiac depolarizations (e.g., P-waves of atrial depolarizationsor R-waves of ventricular depolarizations) when the cardiac EGMamplitude crosses a sensing threshold. For cardiac depolarizationdetection, sensing circuitry 52 may include a rectifier, filter,amplifier, comparator, and/or analog-to-digital converter, in someexamples. In some examples, sensing circuitry 52 may output anindication to processing circuitry 50 in response to sensing of acardiac depolarization. In this manner, processing circuitry 50 mayreceive detected cardiac depolarization indicators corresponding to theoccurrence of detected R-waves and P-waves in the respective chambers ofheart. Processing circuitry 50 may use the indications of detectedR-waves and P-waves for determining inter-depolarization intervals,heart rate, and detecting arrhythmias, such as tachyarrhythmias andasystole.

Sensing circuitry 52 may also provide one or more digitized cardiac EGMsignals to processing circuitry 50 for analysis, e.g., for use indetecting a PVC, and/or for analysis to determine whether one or morePVC storage criteria are satisfied according to the techniques of thisdisclosure. In some examples, processing circuitry 50 may store thedigitized cardiac EGM or one or more portions of the digitized cardiacEGM associated with a PVC in storage device 56. Processing circuitry 50of IMD 10, and/or processing circuitry of another device that retrievesor receives data from IMD 10, may analyze the cardiac EGM to determinewhether one or more PVC storage criteria are satisfied according to thetechniques of this disclosure.

Communication circuitry 54 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as external device 12, another networked computing deviceor server, or another IMD or sensor. Under the control of processingcircuitry 50, communication circuitry 54 may receive downlink telemetryfrom, as well as send uplink telemetry to external device 12 or anotherdevice with the aid of an internal or external antenna, e.g., antenna26. In addition, processing circuitry 50 may communicate with anetworked computing device via an external device (e.g., external device12) and a computer network, such as the Medtronic CareLink® Network.Antenna 26 and communication circuitry 54 may be configured to transmitand/or receive signals via inductive coupling, electromagnetic coupling,Near Field Communication (NFC), Radio Frequency (RF) communication,Bluetooth, Wi-Fi, or other proprietary or non-proprietary wirelesscommunication schemes.

In some examples, storage device 56 includes computer-readableinstructions that, when executed by processing circuitry 50, cause IMD10 and processing circuitry 50 to perform various functions attributedto IMD 10 and processing circuitry 50 herein. Storage device 56 mayinclude any volatile, non-volatile, magnetic, optical, or electricalmedia, such as a random access memory (RAM), read-only memory (ROM),non-volatile RAM (NVRAM), electrically-erasable programmable ROM(EEPROM), flash memory, or any other digital media. Storage device 56may store, as examples, programmed values for one or more operationalparameters of IMD 10 and/or data collected by IMD 10 for transmission toanother device using communication circuitry 54. Data stored by storagedevice 56 and transmitted by communication circuitry 54 to one or moreother devices may include at least a portion of a cardiac EGM signalassociated with one or more PVCs, morphological information about PVCs,a count or other quantification of PVCs, and/or other cardiac EGMinformation, as examples.

FIG. 3 is a conceptual side-view diagram illustrating an exampleconfiguration of IMD 10 of FIGS. 1 and 2. In the example shown in FIG.3, IMD 10 may include a leadless, subcutaneously-implantable monitoringdevice having a housing 15 and an insulative cover 76. Electrode 16A andelectrode 16B may be formed or placed on an outer surface of cover 76.Circuitries 50-62, described above with respect to FIG. 2, may be formedor placed on an inner surface of cover 76, or within housing 15. In theillustrated example, antenna 26 is formed or placed on the inner surfaceof cover 76, but may be formed or placed on the outer surface in someexamples. In some examples, one or more of sensors 62 may be formed orplaced on the outer surface of cover 76. In some examples, insulativecover 76 may be positioned over an open housing 15 such that housing 15and cover 76 enclose antenna 26 and circuitries 50-62, and protect theantenna and circuitries from fluids such as body fluids.

One or more of antenna 26 or circuitries 50-62 may be formed on theinner side of insulative cover 76, such as by using flip-chiptechnology. Insulative cover 76 may be flipped onto a housing 15. Whenflipped and placed onto housing 15, the components of IMD 10 formed onthe inner side of insulative cover 76 may be positioned in a gap 78defined by housing 15. Electrodes 16 may be electrically connected toswitching circuitry 58 through one or more vias (not shown) formedthrough insulative cover 76. Insulative cover 76 may be formed ofsapphire (i.e., corundum), glass, parylene, and/or any other suitableinsulating material. Housing 15 may be formed from titanium or any othersuitable material (e.g., a biocompatible material). Electrodes 16 may beformed from any of stainless steel, titanium, platinum, iridium, oralloys thereof. In addition, electrodes 16 may be coated with a materialsuch as titanium nitride or fractal titanium nitride, although othersuitable materials and coatings for such electrodes may be used.

FIG. 4 is a block diagram illustrating an example configuration ofcomponents of external device 12. In the example of FIG. 4, externaldevice 12 includes processing circuitry 80, communication circuitry 82,storage device 84, and user interface 86.

Processing circuitry 80 may include one or more processors that areconfigured to implement functionality and/or process instructions forexecution within external device 12. For example, processing circuitry80 may be capable of processing instructions stored in storage device84. Processing circuitry 80 may include, for example, microprocessors,DSPs, ASICs, FPGAs, or equivalent discrete or integrated logiccircuitry, or a combination of any of the foregoing devices orcircuitry. Accordingly, processing circuitry 80 may include any suitablestructure, whether in hardware, software, firmware, or any combinationthereof, to perform the functions ascribed herein to processingcircuitry 80.

Communication circuitry 82 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as IMD 10. Under the control of processing circuitry 80,communication circuitry 82 may receive downlink telemetry from, as wellas send uplink telemetry to, IMD 10, or another device. Communicationcircuitry 82 may be configured to transmit or receive signals viainductive coupling, electromagnetic coupling, Near Field Communication(NFC), Radio Frequency (RF) communication, Bluetooth, Wi-Fi, or otherproprietary or non-proprietary wireless communication schemes.Communication circuitry 82 may also be configured to communicate withdevices other than IMD 10 via any of a variety of forms of wired and/orwireless communication and/or network protocols.

Storage device 84 may be configured to store information within externaldevice 12 during operation. Storage device 84 may include acomputer-readable storage medium or computer-readable storage device. Insome examples, storage device 84 includes one or more of a short-termmemory or a long-term memory. Storage device 84 may include, forexample, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories,or forms of EPROM or EEPROM. Storage device may be used to store atleast a portion of a cardiac EGM signal associated with one or morePVCs, morphological information about PVCs, a count or otherquantification of PVCs, and/or other cardiac EGM information receivedfrom IMD 10. In some examples, storage device 84 is used to store dataindicative of instructions for execution by processing circuitry 80.Storage device 84 may be used by software or applications running onexternal device 12 to temporarily store information during programexecution.

Data exchanged between external device 12 and IMD 10 may includeoperational parameters. External device 12 may transmit data includingcomputer readable instructions which, when implemented by IMD 10, maycontrol IMD 10 to change one or more operational parameters and/orexport collected data. For example, external device may receive datafrom IMD 10, including at least a portion of a cardiac EGM signalassociated with one or more PVCs, morphological information about PVCs,a count or other quantification of PVCs (e.g., totals and/or byclassification), and/or other cardiac EGM information, for example. Insome examples, processing circuitry 80 may transmit an instruction toIMD 10 which requests IMD 10 to export collected data (e.g., at least aportion of a cardiac EGM signal associated with one or more PVCs,morphological information about PVCs, a count or other quantification ofPVCs, and/or other cardiac EGM information) to external device 12.Either way, external device 12 may receive the collected data from IMD10 and store the collected data in storage device 84. Processingcircuitry 80 may implement any of the techniques described herein toanalyze cardiac EGMs received from IMD 10, e.g., to determine whetherPVC storage criteria is met to store the collected data from IMD 10 orto transmit the collected data from IMD 10 and/or other PVC data (e.g.,at least a portion of a cardiac EGM signal associated with one or morePVCs, morphological information about PVCs, a count or otherquantification of PVCs, and/or other cardiac EGM information) totransmit to another device (e.g., a server, cloud, data center) over anetwork.

A user, such as a clinician or patient 4, may interact with externaldevice 12 through user interface 86. User interface 86 includes adisplay system (not shown), such as a liquid crystal display (LCD) or alight emitting diode (LED) display or other type of screen, with whichprocessing circuitry 80 may present information related to IMD 10, e.g.,cardiac EGM signals, indications of detections of PVCs, PVC morphologyinformation, and quantifications of detected PVCs, such as aquantification of PVC burden. As described in further detail below, FIG.10 illustrates exemplary PVC information that may be presented to auser. In addition, user interface 86 may include an input mechanism toreceive input from the user. The input mechanisms may include, forexample, any one or more of buttons, a keypad (e.g., an alphanumerickeypad), a peripheral pointing device, a touch screen, or another inputmechanism that allows the user to navigate through user interfacespresented by processing circuitry 80 of external device 12 and provideinput. In other examples, user interface 86 also includes audiocircuitry for providing audible notifications, instructions or othersounds to the user, receiving voice commands from the user, or both.

FIG. 5 is a block diagram illustrating an example system that includesan access point 90, a network 92, external computing devices, such as aserver 94, and one or more other computing devices 100A-100N(collectively, “computing devices 100”), which may be coupled to IMD 10and external device 12 via network 92, in accordance with one or moretechniques described herein. In this example, IMD 10 may usecommunication circuitry 54 to communicate with external device 12 via afirst wireless connection, and to communicate with an access point 90via a second wireless connection. In the example of FIG. 5, access point90, external device 12, server 94, and computing devices 100 areinterconnected and may communicate with each other through network 92.

Access point 90 may include a device that connects to network 92 via anyof a variety of connections, such as telephone dial-up, digitalsubscriber line (DSL), or cable modem connections. In other examples,access point 90 may be coupled to network 92 through different forms ofconnections, including wired or wireless connections. In some examples,access point 90 may be a user device, such as a tablet or smartphone,that may be co-located with the patient. IMD 10 or external device 12may be configured to transmit data, such as PVC detection information,PVC morphology information, PVC quantifications (e.g., PVC burden),and/or cardiac EGM signals, to access point 90 in response to PVCstorage criteria being met. Access point 90 may then communicate thereceived data to server 94 via network 92.

In some cases, server 94 may be configured to provide a secure storagesite for data that has been collected from IMD 10 and/or external device12. In some cases, server 94 may assemble data in web pages or otherdocuments for viewing by trained professionals, such as clinicians, viacomputing devices 100. One or more aspects of the illustrated system ofFIG. 5 may be implemented with general network technology andfunctionality, which may be similar to that provided by the MedtronicCareLink® Network. In some examples, server 94 may comprise one or moreservers, a cloud, one or more databases, and/or a data center.

In some examples, one or more of computing devices 100 may be a tabletor other smart device located with a clinician, by which the clinicianmay program, receive alerts from, and/or interrogate IMD 10. Forexample, the clinician may access data collected by IMD 10 through acomputing device 100, such as when patient 4 is in in between clinicianvisits, to check on a status of a medical condition. In some examples,the clinician may enter instructions for a medical intervention forpatient 4 into an application executed by computing device 100, such asbased on a status of a patient condition determined by IMD 10, externaldevice 12, server 94. or any combination thereof, or based on otherpatient data known to the clinician. Device 100 then may transmit theinstructions for medical intervention to another of computing devices100 located with patient 4 or a caregiver of patient 4. For example,such instructions for medical intervention may include an instruction tochange a drug dosage, timing, or selection, to schedule a visit with theclinician, or to seek medical attention. In further examples, acomputing device 100 may generate an alert to patient 4 based on astatus of a medical condition of patient 4, which may enable patient 4proactively to seek medical attention prior to receiving instructionsfor a medical intervention. In this manner, patient 4 may be empoweredto take action, as needed, to address his or her medical status, whichmay help improve clinical outcomes for patient 4.

In the example illustrated by FIG. 5, server 94 includes a storagedevice 96, e.g., to store data retrieved from IMD 10, and processingcircuitry 98. Although not illustrated in FIG. 5 computing devices 100may similarly include a storage device and processing circuitry.Processing circuitry 98 may include one or more processors that areconfigured to implement functionality and/or process instructions forexecution within server 94. For example, processing circuitry 98 may becapable of processing instructions stored in memory 96. Processingcircuitry 98 may include, for example, microprocessors, DSPs, ASICs,FPGAs, or equivalent discrete or integrated logic circuitry, or acombination of any of the foregoing devices or circuitry. Accordingly,processing circuitry 98 may include any suitable structure, whether inhardware, software, firmware, or any combination thereof, to perform thefunctions ascribed herein to processing circuitry 98. Processingcircuitry 98 of server 94 and/or the processing circuitry of computingdevices 100 may implement any of the techniques described herein toanalyze cardiac EGMs received from IMD 10, e.g., to determine whether tostore or transmit cardiac EGM information associated with a PVC inresponse to one or more PVC storage criteria being met.

Storage device 96 may include a computer-readable storage medium orcomputer-readable storage device. In some examples, memory 96 includesone or more of a short-term memory or a long-term memory. Storage device96 may include, for example, RAM, DRAM, SRAM, magnetic discs, opticaldiscs, flash memories, or forms of EPROM or EEPROM. In some examples,storage device 96 is used to store data indicative of instructions forexecution by processing circuitry 98.

FIG. 6A is a graph illustrating a cardiac EGM signal 120 and an exampletechnique for detecting and classifying PVCs based on the cardiac EGMsignal. For example, the techniques of this disclosure may use differentfeatures such as inter-depolarization (e.g., R-R) interval andmorphology characteristics to distinguish a PVC depolarization from anormal ventricular depolarization. IMD 10 senses cardiac EGM signal 120and detects the timing of ventricular depolarizations 122A, 122B, 122C,and 122D (collectively, “ventricular depolarizations 122”) usingventricular depolarization, e.g., R-wave, detection techniques such asthose described with respect to FIG. 2.

In some examples, IMD 10 senses ventricular depolarizations 122 usingtwo or more, e.g., primary and secondary, sensing channels. Thedifferent sensing channels may have different hardware, differentfirmware settings, and/or different software settings for processingcardiac EGM signal 120 to detect ventricular depolarizations 122. Forexample, a primary sensing channel may implement a relatively shorterblanking, e.g., 150 milliseconds (ms), auto-adjusting threshold havingrelatively higher amplitudes for depolarization detection. For theprimary sensing channel, some examples may implement the techniquesdescribed in U.S. Pat. No. 7,027,858, by Cao et al., which isincorporated herein by reference.

However, because the ventricular depolarization wave, e.g., QRScomplexes, of PVC depolarizations (i.e., PVCs) are typically wider andhave relatively lower frequency content than normal depolarizations, theprimary sensing channel may under sense PVC depolarizations. A secondarysensing channel may include a relatively longer blanking, e.g., 520 ms,fixed threshold, which may facilitate detection of PVC depolarizationsthat may have not been detected by the primary sensing channel.Processing circuitry 50 and/or sensing circuitry 52 may determine thefixed threshold used by the secondary sensing channel to detect adepolarization in a given cardiac cycle based on amplitudes of one ormore prior ventricular depolarizations.

Characteristics that distinguish PVC depolarizations from normalventricular depolarizations include: shorter intervals between a PVCdepolarization and the preceding adjacent (in time) depolarization;longer intervals between a PVC depolarization and a subsequent adjacentdepolarization; and differing depolarization and repolarization wavemorphologies as between PVC depolarizations and normal ventriculardepolarizations. In order to determine whether a current ventriculardepolarization 122C is a PVC depolarization, processing circuitry 50 ofIMD 10 or other processing circuitry of system 2 may consider intervaland morphological information for current ventricular depolarization122C, preceding (in time) adjacent depolarization 122B, and subsequent(in time) adjacent depolarization 122D. The processing circuitry mayiteratively determine whether each of ventricular depolarizations 122 isa PVC depolarization in this manner by proceeding to the nextdepolarization, e.g., depolarization 122C becomes the preceding adjacentdepolarization, depolarization 122D becomes the current depolarization,and the next (in time) depolarization after depolarization 122D becomesthe subsequent adjacent depolarization. Although the techniques fordetermining whether a ventricular depolarization is a PVC depolarizationare described herein primarily as being performed by processingcircuitry 50 of IMD 10, such techniques may be performed, in whole orpart, by processing circuitry of any one or more devices of system 2,such as processing circuitry 80 of external device 12, processingcircuitry 98 of server 94, or processing circuitry of one or morecomputing devices 100.

In some examples, processing circuitry 50 determines respectiveinter-depolarization intervals 124A-124C (collectively“inter-depolarization intervals 124”), e.g., R-R intervals, for each ofdepolarizations 122. For example, processing circuitry 50 may determineinter-depolarization interval 124A for preceding adjacent depolarization122B as the interval between the time of detection of ventriculardepolarization 122A and the time of detection of ventriculardepolarization 122B. Similarly, processing circuitry 50 may determineinter-depolarization interval 124B for current depolarization 122C asthe interval between the time of detection of ventricular depolarization122B and the time of detection of ventricular depolarization 122C, andinter-depolarization interval 124C for subsequent adjacentdepolarization 122D as the interval between the time of detection ofventricular depolarization 122C and the time of detection of ventriculardepolarization 122D.

Processing circuitry 50 may also identify respective segments of adigitized version of cardiac EGM signal 120 for each of ventriculardepolarizations 122B-122D within respective windows 126A-126C(collectively “windows 126”). Each of windows 126 may include apredetermined number of samples, e.g., sixteen samples sampled at 64 Hz,of cardiac EGM signal 120. The locations of the windows 126 and, thus,which samples of cardiac EGM signal 120 are within a given window 126,may be set relative to the time point at which processing circuitry 50detected the corresponding ventricular depolarization 122, or anotherfiducial marker of cardiac EGM signal 120. In some examples, each ofwindows 126 includes sixteen samples of cardiac EGM signal 120 startingfour samples before the point of detection of the respectivedepolarization 122.

To determine whether current ventricular depolarization 122C is a PVCdepolarization, processing circuitry 50 may determine whetherventricular depolarizations 122B-122D satisfy one or more morphologicalcriteria based on the segments within respective windows 126. For eachof depolarizations 122B-122D, processing circuitry 50 may determine, asexamples, one or more of a maximum amplitude, a minimum amplitude, amaximum slope, and a minimum slope within the respective window126A-126C.

Processing circuitry 50 may determine the time interval, e.g., number ofsamples, also referred to herein as the slope interval, between thepoint of the maximum slope and the point of the minimum slope for eachof depolarizations 122B-122D. Processing circuitry 50 may determine theslope of cardiac EGM signal 120 using any known techniques, such as bydetermining a derivative or differential signal of cardiac EGM signal120.

The morphological criteria may include criteria relating the degree ofcorrelation between the various possible pairings of depolarizations122B-122D. Processing circuitry 50 may determine correlation values fora pair of depolarizations by performing a correlation operation with thesegments of cardiac EGM signal 120 within the respective windows 126 forthe depolarizations. Example correlation operations include any knowncross-correlation, wavelet-based comparison, feature set comparison, ordifference sum techniques.

An example formula for computing cross correlation is:

$\begin{matrix}{{C_{xy}(L)} = {\frac{1}{Norm}{\sum\limits_{k = 0}^{N - {L} - 1}\;{\left( {x_{k + {L}} - \overset{\sim}{x}} \right)\left( {y_{k} - \overset{\sim}{y}} \right)}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

where x and y are the two segments of cardiac EGM signal 120 to becompared and different values of L are the different lags over which thecross-correlation is computed. This equation represents shifting one ofthe segments by a lag (L), multiplying it with the other segmentpoint-by-point, and adding the multiplied result point-by-point. Thesame process is followed for different lags. In some examples, the lagsare +/− four samples. The maximum of C(L) will happen at the lag wherethe two segments x and y match the best with each other. In suchexamples, processing circuitry 50 may determine the maximum of C(L) asthe correlation value for a given comparison between two ventriculardepolarizations 122.

In order to conserve the processing and power resources of IMD 10,processing circuitry 50 may implement a difference sum technique fordetermining correlation values representative of the degree ofcorrelation between the various pairings of depolarizations 122B-122D.Processing circuitry 50 may determine a point-by-point differencebetween the segments of cardiac EGM signal 120 for the twodepolarizations 122 at various lags, such as +/− four samples, and thelag which has the lowest difference sum will have the highestcorrelation between the depolarizations 122. An example formula forcomputing the difference sum is:

$\begin{matrix}{{D_{XY}(L)} = {{\sum\limits_{k = 0}^{N - {L} - 1}\; x_{k + {L}}} - y_{k}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

where x and y are the two segments of cardiac EGM signal 120 to becompared and different values of L are the different lags over which thedifference sum is computed. In contrast to C(L), the lowest differencesum value D(L) will occur at the lag where the two segments x and ymatch best with each other. In other words, the lag with the greatestdegree correlation between segments x and y will have the lowestdifference sum value D(L).

In some examples, to determine whether current ventriculardepolarization 122C is a PVC depolarization (e.g., to detect a PVC),processing circuitry 50 determines a correlation value between currentventricular depolarization 122C and each of preceding adjacentventricular depolarization 122B and subsequent adjacent depolarization122D. In the example illustrated by FIG. 6, current ventriculardepolarization 122C is a PVC depolarization and both adjacentventricular depolarizations 122B and 122D are normal ventriculardepolarizations. Since ventricular depolarization 122C has a differentmorphology than both of adjacent ventricular depolarizations 122B and122D, the correlation values determined by processing circuitry 50 forthese two comparisons are both expected to indicate a relatively lowdegree of correlation, e.g., a relatively high difference sum value.Processing circuitry 50 may also determine a correlation value betweenadjacent ventricular depolarizations 122B and 122D. Since ventriculardepolarizations 122B and 122D are both normal ventriculardepolarizations expected to have similar morphologies, the correlationvalue between them is expected to indicate a relative high degree ofcorrelation, e.g., a relatively low difference sum value. Processingcircuitry 50 may apply any combination of one or more of themorphological criteria described herein.

To determine whether current ventricular depolarization 122C is a PVCdepolarization, processing circuitry 50 may also evaluate the respectiveinter-depolarization intervals 124A-124C for ventricular depolarizations122B-122D. Since current ventricular depolarization 122C is a PVCdepolarization, inter-depolarization interval 124B is expected to beshorter than inter-depolarization interval 124A and inter-depolarizationinterval 124C is expected to be longer than inter-depolarizationinterval 124A due to a compensatory pause following the PVCdepolarization. Processing circuitry 50 may also evaluate the maximumand minimum amplitudes, and the slope intervals, for ventriculardepolarizations 122B-122D to determine whether depolarization 122C is aPVC depolarization. Since depolarization 122C is a PVC depolarizationand is expected to have a wide QRS complex, the interval, e.g., numberof samples, between the maximum and minimum slope for depolarization122C is expected to be more than that of a normal depolarization, suchas adjacent depolarizations 122B and 122D. For PVC detection, someexamples may implement the techniques described in U.S. Pat. No.9,675,270, by Sarkar; U.S. Patent Pub. Nos. 2016/0310029 and2016/0310031, by Sarkar; and U.S. patent application Ser. No.16/436,012, by Rajagopal et al., which are incorporated herein byreference.

After determining that current ventricular depolarization 122C is a PVCdepolarization, processing circuitry 50 may classify current ventriculardepolarization 122C. For example, processing circuitry 50 may store aplurality of classifications in storage device 56. In the example shownin FIG. 6A, storage device 56 may contain classifications 1, 2, and 3represented by PVC morphologies 127A, 128A, and 129A, respectively. Insome examples, each of PVC morphologies 127A, 128A, and 129A maycomprise the average or mean morphology of the PVC signals detectedunder the corresponding classification. In other examples, each of theof PVC morphologies 127A, 128A, and 129A may comprise the last PVCsignal classified under the corresponding classification.

To classify ventricular depolarization 122C or the portion of EGM signal120 associated with ventricular depolarization 122C (e.g., the portionof EGM signal 120 contained within window 126B) (herein referred to as“PVC 122C”), processing circuitry 50 may determine a difference betweenPVC 122C and each of PVC morphologies 127A, 128A, and 129A to identifythe closest classification to PVC 122C. For example, processingcircuitry 50 may determine correlation values between PVC 122C and eachof PVC morphologies 127A, 128A, and 129A. To determine these correlationvalues, processing circuitry 50 may perform any of the correlationoperations described above (e.g., cross correlation, wavelet-basedcomparison, feature set comparison, or difference sum techniques). Forexample, processing circuitry 50 may determine correlation valuesbetween PVC 122C and each of PVC morphologies 127A, 128A, and 129A usingthe difference sum technique to identify the closest classification(e.g., the classification with the lowest difference sum). In someexamples, processing circuitry 50 can determine the Euclidean distancebetween PVC 122C and each of the PVC morphologies to identify theclosest classification (e.g., the PVC morphology with the lowestEuclidean distance to PVC 122C). An example formula for computing aEuclidean distance is:

d=√{square root over ((x ₂ −x ₁)²−(y ₂ −y ₁)²)}  Equation 3

where x and y are the two points of PVC 122C and a stored PVCmorphology. In some examples, processing circuitry 50 may determine NEuclidean distances between N different points on PVC 122C and eachstored PVC morphology and either add the N Euclidean distances orcalculate the average Euclidean distance to determine the correlationvalue between PVC 122C and each stored PVC morphology. Either way, thePVC morphology with the lowest Euclidean distance will represent theclosest stored classification to PVC 122C. In some examples, processingcircuitry 50 may classify PVC 122C using a clustering algorithm, such asK-Means clustering, for example.

In the example shown in FIG. 6A, processing circuitry 50 may determinePVC morphology 128A as the closest classification to PVC 122C afterdetermining the correlation values (e.g., the difference sum) betweenPVC 122C and each of PVC morphologies 127A, 128A, and 129A, and findingthat the correlation value of PVC morphology 128A is the lowest of thethree correlation values. While only three classifications are shown inFIG. 6A, processing circuitry 50 may store fewer or more classificationsin accordance with this disclosure. For example, processing circuitry 50may store any number (N) classifications, and processing circuitry 50would determine N correlation values between PVC 122C and each of the Nclassifications to identify the closest classification (e.g., theclassification with the lowest difference sum). Processing circuitry 50may then determine whether the correlation value (e.g., the differencesum) between PVC 122C and PVC morphology 128A is below a thresholdvalue. If the correlation value is equal to or above the thresholdvalue, processing circuitry 50 will classify PVC 122C as a newclassification (e.g., “Classification 4”) and optionally store PVC 122Cas the PVC morphology for that new classification. If the correlationvalue is below the threshold value, processing circuitry 50 willclassify PVC 122C under “Classification 2.” In some examples, when thePVC morphology 128A represents the average or mean morphology of the PVCsignals detected under “Classification 2,” processing circuitry 50 willupdate PVC morphology 128A (e.g., recalculate the mean or average PVCmorphology) to include the morphology of PVC 122C (e.g., as shown in PVCmorphology 128AA of FIG. 6B) if PVC 122C is classified as“Classification 2.”

FIG. 6B illustrates exemplary classified PVC cardiac EGM signals. Inparticular, FIG. 6B shows classifications 1, 2, and 3 represented by PVCmorphologies 127A, 128AA, and 129A, respectively. While only threeclassifications are shown in FIG. 6B, processing circuitry 50 may storefewer or more classifications in accordance with this disclosure. Inthis example, each of PVC morphologies 127A, 128AA, and 129A comprisethe average or mean morphology of the PVC signals detected under thecorresponding classification. For example, PVC morphology 127Arepresents the average or mean of PVC signals 127B, 127C, and 127D; PVCmorphology 128AA represents the average or mean of PVC signals 128B,128C, 128D, and PVC 122C of FIG. 6A; and PVC morphology 129A representsthe average or mean of PVC signals 129B and 129C. In some examples, PVCmorphology 128AA may include PVC 122C of FIG. 6A. In some examples, eachof PVC signals 127B, 127C, 127D, 128B, 128C, 128D, 129B, and 129Ccomprise portions of a cardiac EGM signal corresponding to a PVC (e.g.,the P, QRS, and T waves of the PVC).

In some examples, processing circuitry 50 stores each of PVC signals127A, 127B, 127C, 127D, 128AA, 128B, 128C, 128D, 129A, 129B, and 129C instorage device 56. For example, processing circuitry 50 may store eachof PVC signals 127A, 128AA, and 129A in a buffer in storage device 56.In some examples, each buffer includes a reference to a data structure(e.g., stack, queue, array, linked list, tree, or table), containing thePVC signals detected under the corresponding classification. Forexample, the buffer entry containing PVC morphology 127A may include areference or link to a data structure containing PVC signals 127B, 127C,and 127D; the buffer entry containing PVC morphology 128AA may include areference or link to a data structure containing PVC signals 128B, 128C,and 128D; and the buffer entry containing PVC morphology 129A mayinclude a reference or link to a data structure containing PVC signals129B and 129C. In some examples, processing circuitry 50 may store allor the last N number (e.g., 10, 20) of PVC signals detected under thecorresponding classification. In the examples where processing circuitry50 store only the last N number (e.g., 10, 20) of PVC signals and the Nnumber of PVC signals are stored for a particular classification, theprocessing circuitry removes the oldest stored PVC signal before storinganother PVC signal. In some examples, stored PVC signals are purgedautomatically after a certain period of time (e.g., after a week, amonth, a year, or any other period of time) or manually by the user, aphysician, or an admin.

In some examples, processing circuitry 50 stores each of PVC signals127A, 127B, 127C, 127D, 128AA, 128B, 128C, 128D, 129A, 129B, and 129C ina cloud (e.g., external device 12 and/or 94). In some examples, thecloud may periodically (e.g., every week, month, year, or any otherperiod of time) update the average or mean morphology of the last Nnumber (e.g., 10, 20) of stored PVC signals. In some examples, the cloudmay not purge any stored PVC signals.

In some examples, if a particular classification may not match to anydetected PVC for a sufficiently long period of time (e.g., a 3 months, 6months, a year, or any other period of time), processing circuitry 50may “retire” or “archive” that classification (e.g., processingcircuitry 50 may no longer compare future detected PVCs to thatclassification) as it may not be applicable anymore to the clinicalsituation at present. For example, processing circuitry 50 may keeptrack of how many matches are being generated for a given classificationand when the PVCs were detected.

FIG. 7 is a flow diagram illustrating an example operation fortriggering the storage of a cardiac EGM signal associated with a PVC.Although the example operation of FIG. 7 is described as being performedby processing circuitry 50 of IMD 10 and with respect to cardiac EGMsignal 120 of FIG. 6, in other examples some or all of the exampleoperation may be performed by processing circuitry of another device andwith respect to any cardiac EGM.

According to the example of FIG. 7, processing circuitry 50 senses acardiac EGM signal of a patient (e.g., via electrodes 16) (702). Next,processing circuitry 50 detects a PVC within the cardiac EGM signal(e.g., as described above with reference to FIG. 6A or any other knownmethod of detecting PVCs within a cardiac EGM signal) (704). In someexamples, processing circuitry 50 keeps a count of detected PVCs for aperiod of time (e.g., 6 hours, 24 hours, a week, a month).

Processing circuitry 50 then classifies the detected PVC (706). Asdescribed above with reference to FIG. 6A, to classify PVC 122C,processing circuitry 50 may compare the morphology of PVC 122 to each ofPVC morphologies 127A, 128A, and 129A corresponding to theclassifications stored in storage device 56 to identify the closestclassification to PVC 122C. If the difference between PVC 122C and theclosest classification in storage device 56 is equal to above athreshold difference, processing circuitry 50 will create a newclassification and store PVC 122C as the PVC morphology for that newclassification. If the difference between PVC 122C and the closestclassification in storage device 56 is less than a threshold difference,processing circuitry 50 will classification PVC 122C as the closestclassification. In some examples, processing circuitry 50 can determinethe Euclidean distance between PVC 122C and each of the PVC morphologiesto identify the closest classification (e.g., the PVC morphology withthe lowest Euclidean distance to PVC 122C). In the example shown in FIG.6A, PVC morphology 128A is the closest classification to PVC 112C andthe difference (e.g., the correlation value) between PVC morphology 128Aand PVC 112C is below a threshold value and processing circuitry 50classifies PVC 122C under “Classification 2.” In some examples, when thePVC morphology 128A represents the average or mean morphology of the PVCsignals detected under “Classification 2,” processing circuitry 50 willupdate PVC morphology 128A (e.g., recalculate the mean or average PVCmorphology) to include the morphology of PVC 122C (e.g., as shown in PVCmorphology 128AA of FIG. 6B). In some examples, processing circuitry 50may classify PVC 122C using a clustering algorithm, such as K-Meansclustering, for example. In some examples, processing circuitry 50 keepsa count of detected or other quantification of the PVCs detected for agiven classification (e.g., determines a PVC burden by classification).

Processing circuitry 50 further determines whether the PVC storagecriteria is met (708). In some examples, PVC storage criteria is met ifthe PVC burden exceeds a PVC burden threshold, if a new PVCclassification is detected, if a bigeminy or trigeminy event isdetected, or if an R-on-T phenomenon is detected (e.g., as described infurther detail below with reference to FIG. 8).

Based on a determination that PVC storage criteria is not met (NO branchof 708), processing circuitry 50 may eschew storing the portion ofcardiac EGM signal 120 associated with PVC 122C (710). Based on adetermination that PVC storage criteria is met (YES branch of 708),processing circuitry 50 may store the portion of cardiac EGM signal 120associated with PVC 122C (712). For example, processing circuitry 50 maystore the portion of cardiac EGM signal 120 associated with PVC 122C instorage device 56. In some examples, processing circuitry 50 stores PVC122C with other PVCs occurring within a first period of time (e.g., 24hours, a week, a month). In some examples, processing circuitry 50 maytransmit the portion of cardiac EGM signal 120 associated with PVC 122Cto another device via a network in response to a determination that PVCstorage criteria is met (YES branch of 708). For example, processingcircuitry 50 may transmit the portion of cardiac EGM signal 120associated with PVC 122C to external device 12, server 94, any ofcomputing devices 100, or any other device. In other examples, theportion of cardiac EGM signal 120 associated with PVC 122C may includeone or more other beats around the PVC 122C. For example, processingcircuitry 50 may store or transmit a portion of the cardiac EGM signalincluding PVC 122C with a duration between two and fourteen minutes. Insome examples, processing circuitry 50 may store or transmit other PVCinformation, including PVC burden information (e.g., total and/or byclassification) for a given duration of time (e.g., 24 hours, a week, amonth) or timing information (e.g., start and end time), for example.

FIG. 8 is a flow diagram illustrating an example operation fortriggering the storage of a portion of a cardiac EGM signal associatedwith a PVC. The example operation of FIG. 8 may be an exampleimplementation of element 708 of FIG. 7, and is illustrated as beginningfrom element 706. In other examples, the example operation of FIG. 8 maybe performed as part of another method for triggering the storage of aportion of a cardiac EGM signal associated with a PVC. In some examples,elements 802-808 may be performed in any order. In some examples, one ormore of elements 802-802 need not be performed.

According to the example of FIG. 8, processing circuitry 50 determineswhether a PVC burden threshold is exceeded (802). In some examples,processing circuitry 50 may determine that PVC burden threshold isexceeded if it is above 10% of a period a time (e.g., 6 hours, 12 hours,24 hours, a week, a month). In some examples, processing circuitry 50will determine a PVC burden for all detected PVCs. In some examples,processing circuitry 50 will determine a PVC burden for each PVCclassification stored in storage device 56. For example, processingcircuitry 50 will determine a total PVC burden, a first PVC burden forClassification 1 (PVC morphology 127A), a second PVC burden forClassification 2 (PVC morphology 128A or 128AA), and a third PVC burdenfor Classification 3 (PVC morphology 129A). In this way, processingcircuitry 50 will determine whether any of the total, first, second, orthird PVC burdens exceeds the PVC burden threshold (802). Either way, ifa PVC burden threshold is exceeded (YES branch of 802), processingcircuitry 50 will store the portion of cardiac EGM signal 120 associatedwith the detected PVC (712).

If processing circuitry 50 determines that the PVC burden threshold isnot exceeded (NO branch of 802), processing circuitry 50 determineswhether a new PVC classification is detected (804). As described abovewith reference to FIG. 6A, processing circuitry 50 will detect a newclassification if the closest existing classification to the detectedPVC is too different. For example, if the correlation value (e.g., thedifference sum) between PVC 122C and PVC morphology 128A is equal to orabove a threshold value in the example in FIG. 6A, processing circuitry50 will classify PVC 122C as a new classification (e.g., “Classification4”). If a new PVC classification is detected (e.g., because thecorrelation value between the detected PVC and the closestclassification stored in storage device 56 it too great) (YES branch of804), processing circuitry 50 will store the portion of cardiac EGMsignal 120 associated with the detected PVC (712).

If processing circuitry 50 determines that a new PVC classification isnot detected (NO branch of 804), processing circuitry 50 determineswhether a bigeminy or trigeminy event is detected in a cardiac EGMsignal (806). Processing circuitry 50 will detect a bigeminy event in acardiac EGM signal if each detected normal beat is followed by a PVC orby any other abnormal beat. For example, a beat pattern in a cardiac EGMsignal comprising a normal beat, a PVC, a normal beat, a PVC, and so onwould constitute a bigeminy event (whether or not the detected PVCs areof the same or different classifications). Processing circuitry 50 willdetect a trigeminy event in a cardia EGM signal if processing circuitry50 detects two normal beats followed by a PVC or if processing circuitry50 detects a normal beat followed by two PVCs (YES branch of 806). Forexample, to detect the bigeminy event in cardiac EGM signal 120,processing circuitry 50 may determine that current depolarization 122Cis a PVC and determine whether the previous two depolarizations (e.g.,depolarizations 122A and 122B) were a PVC and a normal beat,respectively (whether or not the detected PVCs are of the same ordifferent classifications). In the example shown in FIG. 6A, processingcircuitry 50 may detect a bigeminy event in cardiac EGM signal 120because ventricular depolarization 122A is a PVC depolarization,ventricular depolarization 122B is a normal depolarization, andventricular depolarization 122C is a PVC. In some examples, processingcircuitry 50 may determine whether a quadrigeminy event occurs atelement 806. Processing circuitry 50 will detect a quadrigeminy event ina cardia EGM signal if each detected normal beat is followed by threeconsecutive PVCs or if every fourth beat is a PVC (whether or not thedetected PVCs are of the same or different classifications). In someexamples, processing circuitry 50 may determine whether a couplet eventoccurs at element 806 if processing circuitry 50 detects two consecutivePVCs in a cardiac EGM signal (whether or not the detected PVCs are ofthe same or different classifications). In some examples, processingcircuitry 50 may determine whether a triplet event occurs at element 806if processing circuitry 50 detects three consecutive PVCs in a cardiacEGM signal (whether or not the detected PVCs are of the same ordifferent classifications). If a bigeminy, a trigeminy, a quadrigeminy,a couplet, or a triplet event is detected in a cardiac EGM signal (YESbranch of 806), processing circuitry 50 will store the portion ofcardiac EGM signal 120 associated with the detected PVCs (712).

If processing circuitry 50 determines that a bigeminy or trigeminy isnot detected in a cardiac EGM signal (NO branch of 806), processingcircuitry 50 determines whether an R-on-T phenomenon is detected (808).Processing circuitry 50 will detect an R-on-T phenomenon when it detectsa PVC (e.g., a PVC depolarization) on the T-wave of the previous beat inthe cardiac EGM signal. An R-on-T phenomenon is a particularly dangerousevent because ventricular fibrillation and death can occur. During theT-wave (repolarization), the heart muscle is very sensitive to outsidestimulus and a strong PVC can send the myocardium into fibrillation. Ifan R-on-T phenomenon is detected in a cardiac EGM signal (YES branch of808), processing circuitry 50 will store the portion of cardiac EGMsignal 120 associated with the detected PVC (712). In some examples,processing circuitry 50 will generate an automated clinician alert inresponse to detecting an R-on-T phenomenon. If processing circuitry 50determines that an R-on-T phenomenon is not detected in a cardiac EGMsignal (NO branch of 808), processing circuitry 50 may eschew storingthe portion of cardiac EGM signal 120 associated with the detected PVC(710).

In some examples, processing circuitry 50 may determine whether one ormore of criteria 802-808 are met. For example, processing circuitry 50may perform elements 802-808 for every detected PVC. In that example,processing circuitry 50 will store which of criteria 802-808 are met inaddition to storing the portion of cardiac EGM signal 120 associatedwith the detected PVC. For example, if a PVC burden threshold isexceeded (YES branch of 802), processing circuitry 50 may store anindication that the PVC burden was exceeded and determines whether a newPVC classification is also detected (804). If processing circuitry 50determines that a new PVC classification is detected (YES branch of804), processing circuitry 50 may store an indication that a new PVCclassification was detected and also determines whether a bigeminy ortrigeminy event is detected in a cardiac EGM signal (806). If processingcircuitry 50 determines that a bigeminy, trigeminy, quadrigeminy,couplet, or triplet event was detected in a cardiac EGM signal (YESbranch of 806), processing circuitry 50 processing circuitry 50 maystore the detected PVCs and an indication that bigeminy, trigeminy,quadrigeminy, couplet, or triplet event was detected and also determineswhether an R-on-T phenomenon is detected (808). In this example,elements 802-808 could be performed serially, concurrently, or in anyorder. FIG. 9 is a flow diagram illustrating an example operation forclassifying PVCs. The example operation of FIG. 9 may be an exampleimplementation of element 706 of FIG. 7. In other examples, the exampleoperation of FIG. 9 may be performed as part of another method fortriggering the storage of a portion of a cardiac EGM signal associatedwith a PVC.

According to the example of FIG. 9, processing circuitry 50 determines adifference between a detected PVC and each stored PCV classification instorage device 56 (902). As shown in the example in FIG. 6A, storagedevice 56 may contain classifications 1, 2, and 3 represented by PVCmorphologies 127A, 128A, and 129A, respectively. In some examples,processing circuitry 50 may determine a difference between PVC 122C andeach of PVC morphologies 127A, 128A, and 129A by determining correlationvalues between PVC 122C and each of PVC morphologies 127A, 128A, and129A. To determine these correlation values, processing circuitry 50 mayperform any of the correlation operations described above with referenceto FIG. 6A (e.g., cross correlation, wavelet-based comparison, featureset comparison, difference sum, or Euclidean distance techniques). Forexample, processing circuitry 50 may determine correlation valuesbetween PVC 122C and each of PVC morphologies using the difference sumtechnique. Based on the correlation values, processing circuitry 50 thenidentifies the closest stored classification to PVC 122C (904). In theexample shown in FIG. 6A, processing circuitry 50 may identify PVCmorphology 128A as the closest classification to PVC 122C (e.g., theclassification with the lowest difference sum).

Processing circuitry 50 may then determines whether the differencebetween the detected PVC and the closest classification is below athreshold value (906). For example, processing circuitry 50 maydetermine whether the correlation value (e.g., the difference sum)between PVC 122C and PVC morphology 128A is below a threshold value.Based on a determination that the correlation value is equal to or abovethe threshold value (NO branch of 906), processing circuitry 50 willclassify PVC 122C as a new classification (e.g., “Classification 4”) andoptionally store PVC 122C as the PVC morphology for that newclassification (910). Based on a determination that the correlationvalue is below the threshold value (YES branch of 906), processingcircuitry 50 will classify PVC 122C under “Classification 2.” In someexamples, when the PVC morphology 128A represents the average or meanmorphology of the PVC signals detected under “Classification 2,”processing circuitry 50 will update PVC morphology 128A (e.g.,recalculate the mean or average PVC morphology) to include themorphology of PVC 122C (e.g., as shown in PVC morphology 128AA of FIG.6B).

FIG. 10 is a graph illustrating example PVC information that may bepresented to a user. For example, graph 1000 may comprise a userinterface for use by a physician, clinical technician, or any other userto review PVC information classified and stored in accordance withtechniques of this disclosure.

In the example shown in FIG. 10, the number of PVCs detected (e.g., thePVC burden) for each day are presented. For example, FIG. 10 illustratesthe total PVC burden on each day with line graph 1008. FIG. 10 alsoillustrates bar graphs with a visual break down of the PVC burden byclassification on each day. For example, the per-day PVC burden forClassification 1 is shown by bars 1002A, 1002B, and 1002C (collectively,“bars 1002”); the per-day PVC burden for Classification 2 is shown bybars 1004A, 1004B, and 1004C (collectively, “bars 1004”); and theper-day PVC burden for Classification 3 is shown by bars 1006A, 1006B,and 1006C (collectively, “bars 1006”). While FIG. 10 shows bars 1002,1004, and 1006 stacked on top of each other in order with the highestPVC burden per day at the bottom and the lowest PVC burden per day atthe top, it is understood that each of bars 1002, 1004, and 1006 may bedisplayed adjacent to each other and/or in any order. In some examples,a physician, clinical technician, or any other user may select any ofbars 1002, 1004, and 1006 and the system will display additional PVCinformation (in the same screen or in a pop up screen). For example, thesystem may display stored portions of the cardiac EGM signalcorresponding to the detected PVCs for that particular day. For example,a user may select bar 1002A and the system may display stored portionsof the cardiac EGM signal corresponding to the detected Classification 1PVCs for 10/1. In this way, the system may help physicians detect andidentify patterns of PVCs to better treat their patients, particularlypatients with existing heart problems. For example, physicians may wantto view and analyze EGM data about the morphologies of the PVCs detectedin a particular patient. This may assist a physician to localize theorigin of the PVCs and/or determine whether there are multiple triggerscausing the PVCs, which may facilitate more accurate determinations ofcardiac wellness and risk of sudden cardiac death, and may lead toclinical interventions to suppress PVCs such as medications.

As described above, processing circuitry, such as processing circuitry50 of IMD 10, may include any combination of one or more of hardware,firmware, and software configured to implement the techniques describedherein. In some examples, implementation of certain aspects of thedescribed techniques in hardware may improve the computation and powerperformance of the implementing device, e.g., IMD 10. As examples,processing circuitry may include hardware configured to computedifference sums or other correlation values, and include firmware forother functionality described herein.

The techniques described in this disclosure may be implemented, at leastin part, in hardware, software, firmware, or any combination thereof.For example, various aspects of the techniques may be implemented withinone or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalentintegrated or discrete logic QRS circuitry, as well as any combinationsof such components, embodied in external devices, such as physician orpatient programmers, stimulators, or other devices. The terms“processor” and “processing circuitry” may generally refer to any of theforegoing logic circuitry, alone or in combination with other logiccircuitry, or any other equivalent circuitry, and alone or incombination with other digital or analog circuitry.

For aspects implemented in software, at least some of the functionalityascribed to the systems and devices described in this disclosure may beembodied as instructions on a computer-readable storage medium such asRAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or formsof EPROM or EEPROM. The instructions may be executed to support one ormore aspects of the functionality described in this disclosure.

In addition, in some aspects, the functionality described herein may beprovided within dedicated hardware and/or software modules. Depiction ofdifferent features as modules or units is intended to highlightdifferent functional aspects and does not necessarily imply that suchmodules or units must be realized by separate hardware or softwarecomponents. Rather, functionality associated with one or more modules orunits may be performed by separate hardware or software components, orintegrated within common or separate hardware or software components.Also, the techniques could be fully implemented in one or more circuitsor logic elements. The techniques of this disclosure may be implementedin a wide variety of devices or apparatuses, including an IMD, anexternal programmer, a combination of an IMD and external programmer, anintegrated circuit (IC) or a set of ICs, and/or discrete electricalcircuitry, residing in an IMD and/or external programmer.

What is claimed is:
 1. A medical system comprising: a plurality ofelectrodes configured to sense a cardiac electrogram (EGM) signal of apatient; and processing circuitry configured to: detect a prematureventricular contraction (PVC) within the cardiac EGM signal; determinewhether PVC storage criteria is met; in response to a determination thatthe PVC storage criteria is met, store a portion of the cardiac EGMsignal associated with the PVC; and in response to a determination thatthe PVC storage criteria is not met, eschew storing the portion of thecardiac EGM signal associated with the PVC.
 2. The medical system ofclaim 1, wherein the processing circuitry is configured to determinethat the PVC storage criteria is met when a PVC burden associated withthe cardiac EGM signal is above a PVC burden threshold; and wherein theprocessing circuitry is configured to determine that the PVC storagecriteria is not met when the PVC burden associated with the cardiac EGMsignal is equal to or below the PVC burden threshold.
 3. The medicalsystem of claim 1, wherein the processing circuitry is configured toclassify the PVC; wherein the processing circuitry is configured todetermine that the PVC storage criteria is met when the PVC isclassified into a new classification not previously stored by themedical system; and wherein the processing circuitry is configured todetermine that the PVC storage criteria is not met when the PVC isclassified into an existing classification stored by the medical system.4. The medical system of claim 3, wherein the processing circuitry isconfigured to: analyze a morphology of the PVC; and classify the PVCbased on the analysis of the morphology of the PVC.
 5. The medicalsystem of claim 1, wherein the processing circuitry is configured toclassify the PVC as one of a plurality of classifications stored by themedical system; and wherein the processing circuitry is furtherconfigured to determine a PVC burden for each of the plurality ofclassifications stored by the medical system.
 6. The medical system ofclaim 1, wherein the processing circuitry is configured to determinethat the PVC storage criteria is met when a PVC burden associated withthe cardiac EGM signal is above a PVC burden threshold; and wherein theprocessing circuitry is further configured to set PVC burden thresholdto the PVC burden.
 7. The medical system of claim 1, wherein the portionof the cardiac EGM signal associated with the PVC includes two or morebeats around the PVC.
 8. The medical system of claim 1, wherein theportion of the cardiac EGM signal associated with the PVC comprises theportion of the cardiac EGM signal including the PVC with a durationbetween two and fourteen minutes.
 9. The medical system of claim 1,wherein the processing circuitry is configured to classify the PVC by:comparing a morphology of the PVC to each of a plurality ofclassifications stored by the medical system, wherein each of theplurality of classifications comprises a mean morphology of detectedPVCs from that classification; determining a first classification with afirst mean morphology closest to the morphology of the PVC; in responseto a determination that a difference between the first mean morphologyand the morphology of the PVC is below a threshold, classifying the PVCas the first classification and updating the first mean morphology toinclude the morphology of the PVC; and in response to a determinationthat the difference between the first mean morphology and the morphologyof the PVC is equal to or above the threshold, classifying the PVC as anew classification and setting a new mean morphology to the morphologyof the PVC.
 10. The medical system of claim 9, further comprising adisplay system to display PVC information to a physician, wherein up Nof the detected PVCs for each of the plurality of classification aredisplayed to the physician.
 11. The medical system of claim 1, whereinstoring the portion of the cardiac EGM signal associated with the PVCcomprises storing the portion of the cardiac EGM signal associated withthe PVC with other PVCs occurring within a first period of time.
 12. Themedical system of claim 1, wherein the processing circuitry isconfigured to classify the PVC by: comparing a morphology of the PVC toeach of a plurality of classifications stored by the medical system in aplurality of buffers, wherein each of the plurality of classificationscomprises a representative morphology of detected PVCs from thatclassification; determining a first classification with a firstmorphology closest to the morphology of the PVC; in response to adetermination that a difference between the first mean morphology andthe morphology of the PVC is below a threshold, setting the firstmorphology to the morphology of the PVC; and in response to adetermination that the difference between the first morphology and themorphology of the PVC is equal to or above the threshold, classifyingthe PVC as a new classification and storing the morphology of the PVC inanother buffer as a new representative morphology for the newclassification.
 13. The medical system of claim 1, wherein theprocessing circuitry is configured to determine that the PVC storagecriteria is met when the PVC is part of a Bigeminy, Trigeminy,Quadrigeminy, Couplet, or Triplet event.
 14. The medical system of claim1, wherein the processing circuitry is configured to determine that thePVC storage criteria is met when an R-on-T phenomenon is detected. 15.The medical system of claim 1, wherein storing the portion of thecardiac EGM signal associated with the PVC comprises transmitting theportion of the cardiac EGM signal associated with the PVC to a server.16. A method comprising: sensing a cardiac electrogram (EGM) signal of apatient via a plurality of electrodes; detecting a premature ventricularcontraction (PVC) within the cardiac EGM signal; determining whether PVCstorage criteria is met; in response to a determination that the PVCstorage criteria is met, storing a portion of the cardiac EGM signalassociated with the PVC; and in response to a determination that the PVCstorage criteria is not met, eschewing storing the portion of thecardiac EGM signal associated with the PVC.
 17. The method of claim 16,wherein: the PVC storage criteria is met when a PVC burden associatedwith the cardiac EGM signal is above a PVC burden threshold; and the PVCstorage criteria is not met when the PVC burden associated with thecardiac EGM signal is equal to or below the PVC burden threshold. 18.The method of claim 16, further comprising classifying the PVC, whereinthe PVC storage criteria is met when the PVC is classified into a newclassification not previously stored by a medical system, and whereinthe PVC storage criteria is not met when the PVC is classified into anexisting classification stored by the medical system.
 19. The method ofclaim 16, further comprising: classifying the PVC, including: comparinga morphology of the PVC to each of a plurality of classifications storedby a medical system, wherein each of the plurality of classificationscomprises a mean morphology of detected PVCs from that classification;determining a first classification with a first mean morphology closestto the morphology of the PVC; in response to a determination that adifference between the first mean morphology and the morphology of thePVC is below a threshold, classifying the PVC as the firstclassification and updating the first mean morphology to include themorphology of the PVC; and in response to a determination that thedifference between the first mean morphology and the morphology of thePVC is equal to or above the threshold, classifying the PVC as a newclassification and setting a new mean morphology to the morphology ofthe PVC.
 20. A non-transitory computer-readable medium comprisinginstructions for causing one or more processors to: sense a cardiacelectrogram (EGM) signal of a patient; detect a premature ventricularcontraction (PVC) within the cardiac EGM signal; determine whether PVCstorage criteria is met; in response to a determination that the PVCstorage criteria is met, store a portion of the cardiac EGM signalassociated with the PVC; and in response to a determination that the PVCstorage criteria is not met, eschew storing the portion of the cardiacEGM signal associated with the PVC.